Overview

Brought to you by YData

Dataset statistics

Number of variables48
Number of observations32264
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.4 MiB
Average record size in memory469.3 B

Variable types

Numeric27
Categorical21

Alerts

ATMAmt is highly overall correlated with DepHigh correlation
Checks is highly overall correlated with DDABal and 2 other fieldsHigh correlation
DDABal is highly overall correlated with Checks and 1 other fieldsHigh correlation
Dep is highly overall correlated with ATMAmt and 2 other fieldsHigh correlation
DepAmt is highly overall correlated with Checks and 2 other fieldsHigh correlation
HMOwn is highly overall correlated with LOResHigh correlation
HMVal is highly overall correlated with IncomeHigh correlation
ILS is highly overall correlated with ILSBalHigh correlation
ILSBal is highly overall correlated with ILSHigh correlation
Income is highly overall correlated with HMValHigh correlation
LORes is highly overall correlated with HMOwnHigh correlation
MM is highly overall correlated with MMBal and 1 other fieldsHigh correlation
MMBal is highly overall correlated with MM and 1 other fieldsHigh correlation
MMCred is highly overall correlated with MM and 1 other fieldsHigh correlation
POS is highly overall correlated with POSAmtHigh correlation
POSAmt is highly overall correlated with POSHigh correlation
CashBk is highly imbalanced (94.9%)Imbalance
NSF is highly imbalanced (57.3%)Imbalance
IRA is highly imbalanced (70.0%)Imbalance
LOC is highly imbalanced (65.9%)Imbalance
ILS is highly imbalanced (71.5%)Imbalance
MTG is highly imbalanced (71.6%)Imbalance
SDB is highly imbalanced (50.4%)Imbalance
Moved is highly imbalanced (80.7%)Imbalance
InArea is highly imbalanced (75.9%)Imbalance
Inv is highly imbalanced (82.7%)Imbalance
ATMAmt is highly skewed (γ1 = 34.60107902)Skewed
CDBal is highly skewed (γ1 = 24.14072299)Skewed
IRABal is highly skewed (γ1 = 37.66691753)Skewed
MTGBal is highly skewed (γ1 = 82.77244732)Skewed
CCBal is highly skewed (γ1 = 89.22293745)Skewed
DepAmt is highly skewed (γ1 = 30.51064837)Skewed
InvBal is highly skewed (γ1 = 124.8755862)Skewed
DDABal has 5987 (18.6%) zerosZeros
Checks has 10455 (32.4%) zerosZeros
NSFAmt has 29455 (91.3%) zerosZeros
Phone has 27087 (84.0%) zerosZeros
Teller has 17656 (54.7%) zerosZeros
SavBal has 17523 (54.3%) zerosZeros
ATMAmt has 13542 (42.0%) zerosZeros
POS has 25546 (79.2%) zerosZeros
POSAmt has 25546 (79.2%) zerosZeros
CDBal has 28204 (87.4%) zerosZeros
IRABal has 30694 (95.1%) zerosZeros
LOCBal has 30439 (94.3%) zerosZeros
ILSBal has 30664 (95.0%) zerosZeros
MMBal has 28557 (88.5%) zerosZeros
MMCred has 30845 (95.6%) zerosZeros
MTGBal has 30684 (95.1%) zerosZeros
CCBal has 21154 (65.6%) zerosZeros
CCPurc has 28742 (89.1%) zerosZeros
Dep has 7023 (21.8%) zerosZeros
DepAmt has 7024 (21.8%) zerosZeros
InvBal has 31786 (98.5%) zerosZeros

Reproduction

Analysis started2024-09-21 05:33:13.661942
Analysis finished2024-09-21 05:33:40.728076
Duration27.07 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

AcctAge
Real number (ℝ)

Distinct442
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7798041
Minimum0.3
Maximum61.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:40.755429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q12.1
median3.9
Q36.3
95-th percentile19.7
Maximum61.5
Range61.2
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation6.329251
Coefficient of variation (CV)1.0950632
Kurtosis8.4900463
Mean5.7798041
Median Absolute Deviation (MAD)1.9
Skewness2.574452
Sum186479.6
Variance40.059418
MonotonicityNot monotonic
2024-09-21T08:33:40.798859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.9 2642
 
8.2%
4.3 1179
 
3.7%
3.8 1046
 
3.2%
0.3 935
 
2.9%
0.8 743
 
2.3%
1.3 616
 
1.9%
4 608
 
1.9%
4.2 608
 
1.9%
1.8 581
 
1.8%
4.1 570
 
1.8%
Other values (432) 22736
70.5%
ValueCountFrequency (%)
0.3 935
2.9%
0.4 464
1.4%
0.5 453
1.4%
0.6 418
1.3%
0.7 399
1.2%
0.8 743
2.3%
0.9 405
1.3%
1 361
 
1.1%
1.1 359
 
1.1%
1.2 343
 
1.1%
ValueCountFrequency (%)
61.5 1
< 0.1%
59.8 1
< 0.1%
56.3 1
< 0.1%
56.2 1
< 0.1%
54.4 1
< 0.1%
53.8 1
< 0.1%
53.3 1
< 0.1%
53.2 1
< 0.1%
52.5 1
< 0.1%
52.4 1
< 0.1%

DDA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
26316 
0
5948 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

Length

2024-09-21T08:33:40.838975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:40.874290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

Most occurring characters

ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26316
81.6%
0 5948
 
18.4%

DDABal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25017
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2170.0167
Minimum-774.83
Maximum278093.83
Zeros5987
Zeros (%)18.6%
Negative198
Negative (%)0.6%
Memory size252.2 KiB
2024-09-21T08:33:40.907466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-774.83
5-th percentile0
Q159.94
median571.82
Q31834.2325
95-th percentile8297.1965
Maximum278093.83
Range278868.66
Interquartile range (IQR)1774.2925

Descriptive statistics

Standard deviation7282.727
Coefficient of variation (CV)3.3560695
Kurtosis390.32788
Mean2170.0167
Median Absolute Deviation (MAD)571.82
Skewness15.694043
Sum70013419
Variance53038113
MonotonicityNot monotonic
2024-09-21T08:33:40.948051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5987
 
18.6%
19.64 4
 
< 0.1%
151.71 4
 
< 0.1%
3.2 4
 
< 0.1%
3.78 3
 
< 0.1%
65.6 3
 
< 0.1%
152.67 3
 
< 0.1%
93.7 3
 
< 0.1%
1078.11 3
 
< 0.1%
971.82 3
 
< 0.1%
Other values (25007) 26247
81.4%
ValueCountFrequency (%)
-774.83 1
< 0.1%
-399.53 1
< 0.1%
-351.4 1
< 0.1%
-336.74 1
< 0.1%
-286.7 1
< 0.1%
-185.63 1
< 0.1%
-182.71 1
< 0.1%
-146.45 1
< 0.1%
-121.06 1
< 0.1%
-109.6 1
< 0.1%
ValueCountFrequency (%)
278093.83 1
< 0.1%
259734.26 1
< 0.1%
252052.54 1
< 0.1%
247783.59 1
< 0.1%
214068.86 1
< 0.1%
210479.55 1
< 0.1%
205766.18 1
< 0.1%
205122.22 1
< 0.1%
194717.71 1
< 0.1%
188938.66 1
< 0.1%

CashBk
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
31769 
1
 
479
2
 
13
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Length

2024-09-21T08:33:40.987558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:41.018481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31769
98.5%
1 479
 
1.5%
2 13
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%

Checks
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2599182
Minimum0
Maximum49
Zeros10455
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.054913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile15
Maximum49
Range49
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1566429
Coefficient of variation (CV)1.2105028
Kurtosis3.7182057
Mean4.2599182
Median Absolute Deviation (MAD)2
Skewness1.7126268
Sum137442
Variance26.590966
MonotonicityNot monotonic
2024-09-21T08:33:41.096627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 10455
32.4%
2 3048
 
9.4%
1 2671
 
8.3%
3 2358
 
7.3%
4 2260
 
7.0%
5 1735
 
5.4%
6 1644
 
5.1%
7 1240
 
3.8%
8 1225
 
3.8%
9 908
 
2.8%
Other values (32) 4720
14.6%
ValueCountFrequency (%)
0 10455
32.4%
1 2671
 
8.3%
2 3048
 
9.4%
3 2358
 
7.3%
4 2260
 
7.0%
5 1735
 
5.4%
6 1644
 
5.1%
7 1240
 
3.8%
8 1225
 
3.8%
9 908
 
2.8%
ValueCountFrequency (%)
49 1
 
< 0.1%
48 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 3
< 0.1%
37 4
< 0.1%
36 3
< 0.1%
35 2
 
< 0.1%
33 4
< 0.1%
32 5
< 0.1%

DirDep
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
22728 
1
9536 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

Length

2024-09-21T08:33:41.134992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:41.164004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

Most occurring characters

ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22728
70.4%
1 9536
29.6%

NSF
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
29455 
1
 
2809

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

Length

2024-09-21T08:33:41.195675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:41.224528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

Most occurring characters

ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29455
91.3%
1 2809
 
8.7%

NSFAmt
Real number (ℝ)

ZEROS 

Distinct1995
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2905464
Minimum0
Maximum666.85
Zeros29455
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.258767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.3285
Maximum666.85
Range666.85
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.363677
Coefficient of variation (CV)6.2708515
Kurtosis423.14591
Mean2.2905464
Median Absolute Deviation (MAD)0
Skewness15.558164
Sum73902.19
Variance206.31521
MonotonicityNot monotonic
2024-09-21T08:33:41.301493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29455
91.3%
5.77 6
 
< 0.1%
3.89 6
 
< 0.1%
7.02 6
 
< 0.1%
3.57 6
 
< 0.1%
3.43 6
 
< 0.1%
4.56 6
 
< 0.1%
4.58 6
 
< 0.1%
3.54 5
 
< 0.1%
6.13 5
 
< 0.1%
Other values (1985) 2757
 
8.5%
ValueCountFrequency (%)
0 29455
91.3%
0.34 1
 
< 0.1%
0.42 1
 
< 0.1%
0.57 1
 
< 0.1%
0.63 1
 
< 0.1%
0.73 1
 
< 0.1%
0.87 1
 
< 0.1%
0.89 1
 
< 0.1%
1.02 1
 
< 0.1%
1.06 1
 
< 0.1%
ValueCountFrequency (%)
666.85 1
< 0.1%
632.84 1
< 0.1%
475.58 1
< 0.1%
416.37 1
< 0.1%
352.46 1
< 0.1%
321.1 1
< 0.1%
311.27 1
< 0.1%
306 1
< 0.1%
296.93 1
< 0.1%
286.54 1
< 0.1%

Phone
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35364493
Minimum0
Maximum30
Zeros27087
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.339557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1281921
Coefficient of variation (CV)3.1901833
Kurtosis61.669436
Mean0.35364493
Median Absolute Deviation (MAD)0
Skewness6.0221988
Sum11410
Variance1.2728175
MonotonicityNot monotonic
2024-09-21T08:33:41.373666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 27087
84.0%
1 2545
 
7.9%
2 1255
 
3.9%
3 602
 
1.9%
4 293
 
0.9%
5 169
 
0.5%
6 119
 
0.4%
7 55
 
0.2%
8 46
 
0.1%
9 27
 
0.1%
Other values (10) 66
 
0.2%
ValueCountFrequency (%)
0 27087
84.0%
1 2545
 
7.9%
2 1255
 
3.9%
3 602
 
1.9%
4 293
 
0.9%
5 169
 
0.5%
6 119
 
0.4%
7 55
 
0.2%
8 46
 
0.1%
9 27
 
0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
22 2
 
< 0.1%
20 2
 
< 0.1%
17 3
 
< 0.1%
15 3
 
< 0.1%
14 1
 
< 0.1%
13 8
 
< 0.1%
12 11
< 0.1%
11 12
< 0.1%
10 23
0.1%

Teller
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3652678
Minimum0
Maximum27
Zeros17656
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.409980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum27
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2837649
Coefficient of variation (CV)1.6727596
Kurtosis12.08241
Mean1.3652678
Median Absolute Deviation (MAD)0
Skewness2.8097136
Sum44049
Variance5.2155819
MonotonicityNot monotonic
2024-09-21T08:33:41.447522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 17656
54.7%
1 4918
 
15.2%
2 3240
 
10.0%
3 2108
 
6.5%
4 1456
 
4.5%
5 992
 
3.1%
6 620
 
1.9%
7 414
 
1.3%
8 287
 
0.9%
9 180
 
0.6%
Other values (17) 393
 
1.2%
ValueCountFrequency (%)
0 17656
54.7%
1 4918
 
15.2%
2 3240
 
10.0%
3 2108
 
6.5%
4 1456
 
4.5%
5 992
 
3.1%
6 620
 
1.9%
7 414
 
1.3%
8 287
 
0.9%
9 180
 
0.6%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 3
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 4
 
< 0.1%
21 4
 
< 0.1%
20 5
< 0.1%
19 8
< 0.1%
18 8
< 0.1%
17 10
< 0.1%

Sav
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
17200 
1
15064 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

Length

2024-09-21T08:33:41.485089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:41.514090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

Most occurring characters

ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17200
53.3%
1 15064
46.7%

SavBal
Real number (ℝ)

ZEROS 

Distinct14154
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3170.6037
Minimum0
Maximum700026.94
Zeros17523
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.625658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31215.815
95-th percentile15279.469
Maximum700026.94
Range700026.94
Interquartile range (IQR)1215.815

Descriptive statistics

Standard deviation13397.147
Coefficient of variation (CV)4.2254246
Kurtosis507.0017
Mean3170.6037
Median Absolute Deviation (MAD)0
Skewness16.264136
Sum1.0229636 × 108
Variance1.7948355 × 108
MonotonicityNot monotonic
2024-09-21T08:33:41.669288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17523
54.3%
0.01 16
 
< 0.1%
0.03 11
 
< 0.1%
0.04 11
 
< 0.1%
0.02 10
 
< 0.1%
0.12 9
 
< 0.1%
0.09 8
 
< 0.1%
0.07 7
 
< 0.1%
0.11 5
 
< 0.1%
0.16 5
 
< 0.1%
Other values (14144) 14659
45.4%
ValueCountFrequency (%)
0 17523
54.3%
0.01 16
 
< 0.1%
0.02 10
 
< 0.1%
0.03 11
 
< 0.1%
0.04 11
 
< 0.1%
0.05 4
 
< 0.1%
0.06 5
 
< 0.1%
0.07 7
 
< 0.1%
0.08 2
 
< 0.1%
0.09 8
 
< 0.1%
ValueCountFrequency (%)
700026.94 1
< 0.1%
609587.72 1
< 0.1%
409776.97 1
< 0.1%
346059.27 1
< 0.1%
327083.18 1
< 0.1%
317437.98 1
< 0.1%
306453.15 1
< 0.1%
303899.62 1
< 0.1%
293099.86 1
< 0.1%
283499.98 1
< 0.1%

ATM
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
19679 
0
12585 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

Length

2024-09-21T08:33:41.709721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:41.738957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

Most occurring characters

ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19679
61.0%
0 12585
39.0%

ATMAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct17886
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1235.4147
Minimum0
Maximum427731.26
Zeros13542
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.773404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median136.275
Q31202.7975
95-th percentile5023.15
Maximum427731.26
Range427731.26
Interquartile range (IQR)1202.7975

Descriptive statistics

Standard deviation4462.2819
Coefficient of variation (CV)3.611971
Kurtosis2712.1869
Mean1235.4147
Median Absolute Deviation (MAD)136.275
Skewness34.601079
Sum39859419
Variance19911960
MonotonicityNot monotonic
2024-09-21T08:33:41.815479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13542
42.0%
5.8 6
 
< 0.1%
11.03 5
 
< 0.1%
5.82 5
 
< 0.1%
11.33 5
 
< 0.1%
80.89 5
 
< 0.1%
11.21 5
 
< 0.1%
11.39 5
 
< 0.1%
10.81 5
 
< 0.1%
5.64 4
 
< 0.1%
Other values (17876) 18677
57.9%
ValueCountFrequency (%)
0 13542
42.0%
0.6 1
 
< 0.1%
1.39 1
 
< 0.1%
1.43 1
 
< 0.1%
3.7 1
 
< 0.1%
4.04 1
 
< 0.1%
5.08 1
 
< 0.1%
5.34 1
 
< 0.1%
5.35 3
 
< 0.1%
5.36 2
 
< 0.1%
ValueCountFrequency (%)
427731.26 1
< 0.1%
130071.76 1
< 0.1%
127403.36 1
< 0.1%
96863.42 1
< 0.1%
96370.46 1
< 0.1%
94645.47 1
< 0.1%
94257.37 1
< 0.1%
91323.47 1
< 0.1%
90777.08 1
< 0.1%
85982.49 1
< 0.1%

POS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93788743
Minimum0
Maximum54
Zeros25546
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.855423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7211675
Coefficient of variation (CV)2.9013796
Kurtosis36.011294
Mean0.93788743
Median Absolute Deviation (MAD)0
Skewness4.898138
Sum30260
Variance7.4047524
MonotonicityNot monotonic
2024-09-21T08:33:41.895676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 25546
79.2%
1 1731
 
5.4%
2 1201
 
3.7%
4 734
 
2.3%
3 667
 
2.1%
5 524
 
1.6%
6 445
 
1.4%
8 272
 
0.8%
7 219
 
0.7%
9 210
 
0.7%
Other values (30) 715
 
2.2%
ValueCountFrequency (%)
0 25546
79.2%
1 1731
 
5.4%
2 1201
 
3.7%
3 667
 
2.1%
4 734
 
2.3%
5 524
 
1.6%
6 445
 
1.4%
7 219
 
0.7%
8 272
 
0.8%
9 210
 
0.7%
ValueCountFrequency (%)
54 1
 
< 0.1%
47 1
 
< 0.1%
43 1
 
< 0.1%
38 1
 
< 0.1%
36 2
< 0.1%
35 1
 
< 0.1%
34 3
< 0.1%
33 2
< 0.1%
32 2
< 0.1%
30 2
< 0.1%

POSAmt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6163
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.658763
Minimum0
Maximum3293.49
Zeros25546
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:41.935646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile274.507
Maximum3293.49
Range3293.49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation129.01181
Coefficient of variation (CV)3.0242746
Kurtosis58.064611
Mean42.658763
Median Absolute Deviation (MAD)0
Skewness5.7282807
Sum1376342.3
Variance16644.048
MonotonicityNot monotonic
2024-09-21T08:33:41.977212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25546
79.2%
79.88 4
 
< 0.1%
80.61 4
 
< 0.1%
80.58 4
 
< 0.1%
38.38 4
 
< 0.1%
76.73 4
 
< 0.1%
64.5 3
 
< 0.1%
57.8 3
 
< 0.1%
28.27 3
 
< 0.1%
30.75 3
 
< 0.1%
Other values (6153) 6686
 
20.7%
ValueCountFrequency (%)
0 25546
79.2%
5.47 1
 
< 0.1%
6.22 1
 
< 0.1%
6.88 1
 
< 0.1%
7.72 1
 
< 0.1%
7.99 1
 
< 0.1%
8.11 1
 
< 0.1%
8.16 1
 
< 0.1%
8.2 1
 
< 0.1%
8.33 1
 
< 0.1%
ValueCountFrequency (%)
3293.49 1
< 0.1%
2933.83 1
< 0.1%
2608.43 1
< 0.1%
2546.25 1
< 0.1%
2173.67 1
< 0.1%
1885.51 1
< 0.1%
1703.69 1
< 0.1%
1656.18 1
< 0.1%
1626.87 1
< 0.1%
1625.25 1
< 0.1%

CD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
28204 
1
4060 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

Length

2024-09-21T08:33:42.015826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.044558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28204
87.4%
1 4060
 
12.6%

CDBal
Real number (ℝ)

SKEWED  ZEROS 

Distinct690
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2530.7091
Minimum0
Maximum1053900
Zeros28204
Zeros (%)87.4%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.080516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10000
Maximum1053900
Range1053900
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14811.781
Coefficient of variation (CV)5.8528184
Kurtosis1154.4064
Mean2530.7091
Median Absolute Deviation (MAD)0
Skewness24.140723
Sum81650800
Variance2.1938886 × 108
MonotonicityNot monotonic
2024-09-21T08:33:42.125241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28204
87.4%
8000 160
 
0.5%
4000 86
 
0.3%
8100 80
 
0.2%
800 69
 
0.2%
8200 67
 
0.2%
8400 61
 
0.2%
8300 61
 
0.2%
1600 58
 
0.2%
16000 57
 
0.2%
Other values (680) 3361
 
10.4%
ValueCountFrequency (%)
0 28204
87.4%
300 23
 
0.1%
400 7
 
< 0.1%
500 31
 
0.1%
600 3
 
< 0.1%
700 5
 
< 0.1%
800 69
 
0.2%
900 5
 
< 0.1%
1000 6
 
< 0.1%
1100 25
 
0.1%
ValueCountFrequency (%)
1053900 1
< 0.1%
688700 1
< 0.1%
613600 1
< 0.1%
455000 1
< 0.1%
388800 1
< 0.1%
386700 1
< 0.1%
339600 1
< 0.1%
333300 1
< 0.1%
331900 1
< 0.1%
320000 1
< 0.1%

IRA
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
30545 
1
 
1719

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

Length

2024-09-21T08:33:42.164373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.193472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30545
94.7%
1 1719
 
5.3%

IRABal
Real number (ℝ)

SKEWED  ZEROS 

Distinct1561
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.57045
Minimum0
Maximum596497.6
Zeros30694
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.225918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum596497.6
Range596497.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7516.5648
Coefficient of variation (CV)12.171186
Kurtosis2111.782
Mean617.57045
Median Absolute Deviation (MAD)0
Skewness37.666918
Sum19925293
Variance56498747
MonotonicityNot monotonic
2024-09-21T08:33:42.269083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30694
95.1%
0.01 4
 
< 0.1%
0.42 3
 
< 0.1%
6295.21 2
 
< 0.1%
0.02 2
 
< 0.1%
1977.77 2
 
< 0.1%
0.2 2
 
< 0.1%
0.1 2
 
< 0.1%
4900.85 1
 
< 0.1%
10574.51 1
 
< 0.1%
Other values (1551) 1551
 
4.8%
ValueCountFrequency (%)
0 30694
95.1%
0.01 4
 
< 0.1%
0.02 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.1 2
 
< 0.1%
0.12 1
 
< 0.1%
0.18 1
 
< 0.1%
0.2 2
 
< 0.1%
0.21 1
 
< 0.1%
ValueCountFrequency (%)
596497.6 1
< 0.1%
417021.52 1
< 0.1%
415656.63 1
< 0.1%
285339.77 1
< 0.1%
266220.76 1
< 0.1%
265019.9 1
< 0.1%
233757.2 1
< 0.1%
220761.53 1
< 0.1%
200389.81 1
< 0.1%
197663.61 1
< 0.1%

LOC
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
30219 
1
 
2045

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

Length

2024-09-21T08:33:42.309113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.337456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30219
93.7%
1 2045
 
6.3%

LOCBal
Real number (ℝ)

ZEROS 

Distinct1813
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1175.2194
Minimum-613
Maximum523147.24
Zeros30439
Zeros (%)94.3%
Negative15
Negative (%)< 0.1%
Memory size252.2 KiB
2024-09-21T08:33:42.371567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-613
5-th percentile0
Q10
median0
Q30
95-th percentile258.11
Maximum523147.24
Range523760.24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9605.2845
Coefficient of variation (CV)8.1731844
Kurtosis642.15685
Mean1175.2194
Median Absolute Deviation (MAD)0
Skewness19.864465
Sum37917277
Variance92261490
MonotonicityNot monotonic
2024-09-21T08:33:42.414808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30439
94.3%
0.02 3
 
< 0.1%
0.01 3
 
< 0.1%
-0.02 2
 
< 0.1%
0.06 2
 
< 0.1%
396.07 2
 
< 0.1%
0.04 2
 
< 0.1%
6.65 2
 
< 0.1%
25.23 2
 
< 0.1%
9.38 2
 
< 0.1%
Other values (1803) 1805
 
5.6%
ValueCountFrequency (%)
-613 1
< 0.1%
-71.61 1
< 0.1%
-18.54 1
< 0.1%
-16.4 1
< 0.1%
-6.86 1
< 0.1%
-0.69 1
< 0.1%
-0.25 1
< 0.1%
-0.23 1
< 0.1%
-0.21 1
< 0.1%
-0.18 1
< 0.1%
ValueCountFrequency (%)
523147.24 1
< 0.1%
367098.2 1
< 0.1%
363133.1 1
< 0.1%
353824.24 1
< 0.1%
314552.5 1
< 0.1%
292563.11 1
< 0.1%
272553.4 1
< 0.1%
251774.61 1
< 0.1%
250979.29 1
< 0.1%
201643.53 1
< 0.1%

ILS
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
30664 
1
 
1600

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

Length

2024-09-21T08:33:42.454654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.483264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30664
95.0%
1 1600
 
5.0%

ILSBal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1583
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean517.56923
Minimum0
Maximum29162.79
Zeros30664
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.516481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum29162.79
Range29162.79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2285.1871
Coefficient of variation (CV)4.4152297
Kurtosis18.261148
Mean517.56923
Median Absolute Deviation (MAD)0
Skewness4.3394264
Sum16698854
Variance5222079.9
MonotonicityNot monotonic
2024-09-21T08:33:42.558343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30664
95.0%
10080.08 2
 
< 0.1%
10216.85 2
 
< 0.1%
10025.1 2
 
< 0.1%
10023.32 2
 
< 0.1%
10149.56 2
 
< 0.1%
10150.23 2
 
< 0.1%
10050.77 2
 
< 0.1%
10235.07 2
 
< 0.1%
10059.81 2
 
< 0.1%
Other values (1573) 1582
 
4.9%
ValueCountFrequency (%)
0 30664
95.0%
9690.09 1
 
< 0.1%
9746.76 1
 
< 0.1%
9747.58 1
 
< 0.1%
9753.42 1
 
< 0.1%
9762.51 1
 
< 0.1%
9764.8 1
 
< 0.1%
9774.45 1
 
< 0.1%
9777.62 1
 
< 0.1%
9787.52 1
 
< 0.1%
ValueCountFrequency (%)
29162.79 1
< 0.1%
26806.59 1
< 0.1%
24634.63 1
< 0.1%
24323.12 1
< 0.1%
23920.97 1
< 0.1%
23156.66 1
< 0.1%
22214.63 1
< 0.1%
20327.67 1
< 0.1%
19492.97 1
< 0.1%
17472.25 1
< 0.1%

MM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
28557 
1
3707 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

Length

2024-09-21T08:33:42.597185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.626548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28557
88.5%
1 3707
 
11.5%

MMBal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3668
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1875.7632
Minimum0
Maximum120801.11
Zeros28557
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.661099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15237.734
Maximum120801.11
Range120801.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5484.4796
Coefficient of variation (CV)2.9238656
Kurtosis24.476454
Mean1875.7632
Median Absolute Deviation (MAD)0
Skewness3.612812
Sum60519624
Variance30079516
MonotonicityNot monotonic
2024-09-21T08:33:42.705281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28557
88.5%
15009.34 2
 
< 0.1%
15032.1 2
 
< 0.1%
15095.32 2
 
< 0.1%
15039.94 2
 
< 0.1%
15036.32 2
 
< 0.1%
15108.58 2
 
< 0.1%
16416.8 2
 
< 0.1%
15708.15 2
 
< 0.1%
14993.58 2
 
< 0.1%
Other values (3658) 3689
 
11.4%
ValueCountFrequency (%)
0 28557
88.5%
940.92 1
 
< 0.1%
955.75 1
 
< 0.1%
1008.6 1
 
< 0.1%
1014.44 1
 
< 0.1%
1031.14 1
 
< 0.1%
1050.83 1
 
< 0.1%
1082 1
 
< 0.1%
3013.34 1
 
< 0.1%
3345.12 1
 
< 0.1%
ValueCountFrequency (%)
120801.11 1
< 0.1%
107028.55 1
< 0.1%
83708.03 1
< 0.1%
74759.18 1
< 0.1%
71356.94 1
< 0.1%
68965.92 1
< 0.1%
65967.26 1
< 0.1%
64096.16 1
< 0.1%
59833.12 1
< 0.1%
59236.83 1
< 0.1%

MMCred
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.056378626
Minimum0
Maximum5
Zeros30845
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.739845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2874843
Coefficient of variation (CV)5.0991717
Kurtosis47.913755
Mean0.056378626
Median Absolute Deviation (MAD)0
Skewness6.2232719
Sum1819
Variance0.082647221
MonotonicityNot monotonic
2024-09-21T08:33:42.771529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 30845
95.6%
1 1080
 
3.3%
2 288
 
0.9%
3 45
 
0.1%
5 4
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
0 30845
95.6%
1 1080
 
3.3%
2 288
 
0.9%
3 45
 
0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
5 4
 
< 0.1%
4 2
 
< 0.1%
3 45
 
0.1%
2 288
 
0.9%
1 1080
 
3.3%
0 30845
95.6%

MTG
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
30672 
1
 
1592

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

Length

2024-09-21T08:33:42.805678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.834732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30672
95.1%
1 1592
 
4.9%

MTGBal
Real number (ℝ)

SKEWED  ZEROS 

Distinct1581
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8081.736
Minimum0
Maximum10887573
Zeros30684
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:42.869405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10887573
Range10887573
Interquartile range (IQR)0

Descriptive statistics

Standard deviation79794.116
Coefficient of variation (CV)9.873388
Kurtosis10782.473
Mean8081.736
Median Absolute Deviation (MAD)0
Skewness82.772447
Sum2.6074913 × 108
Variance6.3671009 × 109
MonotonicityNot monotonic
2024-09-21T08:33:42.914338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30684
95.1%
31311.62 1
 
< 0.1%
233898.51 1
 
< 0.1%
75818.8 1
 
< 0.1%
161148.94 1
 
< 0.1%
346066.23 1
 
< 0.1%
62.64 1
 
< 0.1%
112655.17 1
 
< 0.1%
390245.1 1
 
< 0.1%
136883.11 1
 
< 0.1%
Other values (1571) 1571
 
4.9%
ValueCountFrequency (%)
0 30684
95.1%
62.64 1
 
< 0.1%
179.08 1
 
< 0.1%
475.49 1
 
< 0.1%
492.8 1
 
< 0.1%
638.42 1
 
< 0.1%
1017.89 1
 
< 0.1%
1209.51 1
 
< 0.1%
1395.73 1
 
< 0.1%
1533.42 1
 
< 0.1%
ValueCountFrequency (%)
10887573.28 1
< 0.1%
2409908.24 1
< 0.1%
1789737.52 1
< 0.1%
1722670.2 1
< 0.1%
1628532.38 1
< 0.1%
1618065.41 1
< 0.1%
1550473.48 1
< 0.1%
1237499.9 1
< 0.1%
1202195.14 1
< 0.1%
1156708.17 1
< 0.1%

CC
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
18674 
1
13590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

Length

2024-09-21T08:33:42.953505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:42.982860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

Most occurring characters

ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18674
57.9%
1 13590
42.1%

CCBal
Real number (ℝ)

SKEWED  ZEROS 

Distinct10818
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8358.5206
Minimum-2060.51
Maximum10641355
Zeros21154
Zeros (%)65.6%
Negative125
Negative (%)0.4%
Memory size252.2 KiB
2024-09-21T08:33:43.018614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2060.51
5-th percentile0
Q10
median0
Q3218.05
95-th percentile32776.589
Maximum10641355
Range10643415
Interquartile range (IQR)218.05

Descriptive statistics

Standard deviation76060.1
Coefficient of variation (CV)9.0997084
Kurtosis11938.558
Mean8358.5206
Median Absolute Deviation (MAD)0
Skewness89.222937
Sum2.6967931 × 108
Variance5.7851388 × 109
MonotonicityNot monotonic
2024-09-21T08:33:43.138884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21154
65.6%
14.47 4
 
< 0.1%
25.51 3
 
< 0.1%
0.02 3
 
< 0.1%
12.95 3
 
< 0.1%
462.3 3
 
< 0.1%
2.84 3
 
< 0.1%
336.99 3
 
< 0.1%
4.54 3
 
< 0.1%
36.75 3
 
< 0.1%
Other values (10808) 11082
34.3%
ValueCountFrequency (%)
-2060.51 1
< 0.1%
-1903.99 1
< 0.1%
-1096.66 1
< 0.1%
-897.46 1
< 0.1%
-698.55 1
< 0.1%
-577.53 1
< 0.1%
-429.1 1
< 0.1%
-375.12 1
< 0.1%
-311.88 1
< 0.1%
-234.35 1
< 0.1%
ValueCountFrequency (%)
10641354.78 1
< 0.1%
2820820.27 1
< 0.1%
1848441.07 1
< 0.1%
1593806.74 1
< 0.1%
1576808.43 1
< 0.1%
1524355.79 1
< 0.1%
1384201.82 1
< 0.1%
1158581.81 1
< 0.1%
1101161 1
< 0.1%
1066037.06 1
< 0.1%

CCPurc
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13442227
Minimum0
Maximum5
Zeros28742
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.173523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42091475
Coefficient of variation (CV)3.1312874
Kurtosis16.884232
Mean0.13442227
Median Absolute Deviation (MAD)0
Skewness3.7349493
Sum4337
Variance0.17716922
MonotonicityNot monotonic
2024-09-21T08:33:43.204673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 28742
89.1%
1 2850
 
8.8%
2 550
 
1.7%
3 103
 
0.3%
4 17
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
0 28742
89.1%
1 2850
 
8.8%
2 550
 
1.7%
3 103
 
0.3%
4 17
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 17
 
0.1%
3 103
 
0.3%
2 550
 
1.7%
1 2850
 
8.8%
0 28742
89.1%

SDB
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
28758 
1
3506 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Length

2024-09-21T08:33:43.238299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:43.267590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28758
89.1%
1 3506
 
10.9%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.587342
Minimum0
Maximum233
Zeros30
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.301328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q123
median35
Q349
95-th percentile91
Maximum233
Range233
Interquartile range (IQR)26

Descriptive statistics

Standard deviation25.922551
Coefficient of variation (CV)0.6548192
Kurtosis4.3579365
Mean39.587342
Median Absolute Deviation (MAD)13
Skewness1.6562366
Sum1277246
Variance671.97867
MonotonicityNot monotonic
2024-09-21T08:33:43.340992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 6408
 
19.9%
20 782
 
2.4%
15 736
 
2.3%
25 722
 
2.2%
30 693
 
2.1%
40 590
 
1.8%
10 582
 
1.8%
45 504
 
1.6%
19 450
 
1.4%
18 439
 
1.4%
Other values (193) 20358
63.1%
ValueCountFrequency (%)
0 30
 
0.1%
1 46
 
0.1%
2 88
 
0.3%
3 144
0.4%
4 185
0.6%
5 343
1.1%
6 247
0.8%
7 249
0.8%
8 298
0.9%
9 338
1.0%
ValueCountFrequency (%)
233 1
< 0.1%
232 1
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
220 1
< 0.1%
218 1
< 0.1%
215 1
< 0.1%
210 1
< 0.1%
207 1
< 0.1%
206 1
< 0.1%

HMOwn
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
20018 
0
12246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

Length

2024-09-21T08:33:43.378151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:43.407106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

Most occurring characters

ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20018
62.0%
0 12246
38.0%

LORes
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9150446
Minimum0.5
Maximum19.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.439889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3
Q15
median6.5
Q38.5
95-th percentile11.5
Maximum19.5
Range19
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.578466
Coefficient of variation (CV)0.37287771
Kurtosis0.38996347
Mean6.9150446
Median Absolute Deviation (MAD)1.5
Skewness0.59932307
Sum223107
Variance6.648487
MonotonicityNot monotonic
2024-09-21T08:33:43.482076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6.5 7333
22.7%
5 1708
 
5.3%
5.5 1689
 
5.2%
4.5 1681
 
5.2%
4 1667
 
5.2%
6 1659
 
5.1%
7 1519
 
4.7%
7.5 1510
 
4.7%
8 1507
 
4.7%
3.5 1380
 
4.3%
Other values (29) 10611
32.9%
ValueCountFrequency (%)
0.5 2
 
< 0.1%
1 15
 
< 0.1%
1.5 90
 
0.3%
2 289
 
0.9%
2.5 564
 
1.7%
3 973
3.0%
3.5 1380
4.3%
4 1667
5.2%
4.5 1681
5.2%
5 1708
5.3%
ValueCountFrequency (%)
19.5 1
 
< 0.1%
19 1
 
< 0.1%
18.5 4
 
< 0.1%
18 5
 
< 0.1%
17.5 10
 
< 0.1%
17 8
 
< 0.1%
16.5 14
 
< 0.1%
16 21
 
0.1%
15.5 30
0.1%
15 59
0.2%

HMVal
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.21104
Minimum67
Maximum754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.524495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile87
Q1100
median107
Q3117
95-th percentile143
Maximum754
Range687
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.942541
Coefficient of variation (CV)0.18094867
Kurtosis109.41518
Mean110.21104
Median Absolute Deviation (MAD)8
Skewness5.7655761
Sum3555849
Variance397.70493
MonotonicityNot monotonic
2024-09-21T08:33:43.566492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 6427
 
19.9%
102 711
 
2.2%
99 680
 
2.1%
100 678
 
2.1%
106 677
 
2.1%
104 677
 
2.1%
101 676
 
2.1%
98 663
 
2.1%
103 657
 
2.0%
97 641
 
2.0%
Other values (193) 19777
61.3%
ValueCountFrequency (%)
67 2
 
< 0.1%
68 1
 
< 0.1%
69 2
 
< 0.1%
70 8
 
< 0.1%
71 4
 
< 0.1%
72 5
 
< 0.1%
73 14
< 0.1%
74 16
< 0.1%
75 28
0.1%
76 31
0.1%
ValueCountFrequency (%)
754 1
< 0.1%
625 1
< 0.1%
610 1
< 0.1%
598 1
< 0.1%
589 1
< 0.1%
515 1
< 0.1%
488 1
< 0.1%
462 1
< 0.1%
458 1
< 0.1%
412 1
< 0.1%

Age
Real number (ℝ)

Distinct79
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.942444
Minimum16
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.608563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile26
Q140
median48
Q355
95-th percentile70
Maximum94
Range78
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.644511
Coefficient of variation (CV)0.26374356
Kurtosis0.35734486
Mean47.942444
Median Absolute Deviation (MAD)7
Skewness0.18130127
Sum1546815
Variance159.88365
MonotonicityNot monotonic
2024-09-21T08:33:43.652046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 7105
 
22.0%
44 715
 
2.2%
51 707
 
2.2%
46 699
 
2.2%
50 695
 
2.2%
45 689
 
2.1%
49 672
 
2.1%
53 666
 
2.1%
47 664
 
2.1%
41 657
 
2.0%
Other values (69) 18995
58.9%
ValueCountFrequency (%)
16 65
 
0.2%
17 72
 
0.2%
18 86
 
0.3%
19 105
0.3%
20 127
0.4%
21 130
0.4%
22 174
0.5%
23 190
0.6%
24 230
0.7%
25 255
0.8%
ValueCountFrequency (%)
94 8
 
< 0.1%
93 1
 
< 0.1%
92 11
< 0.1%
91 12
< 0.1%
90 11
< 0.1%
89 15
< 0.1%
88 21
0.1%
87 26
0.1%
86 25
0.1%
85 25
0.1%

CRScore
Real number (ℝ)

Distinct285
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.50462
Minimum509
Maximum820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:43.695551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum509
5-th percentile600
Q1640
median667
Q3693
95-th percentile732
Maximum820
Range311
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.968053
Coefficient of variation (CV)0.059966656
Kurtosis0.079399312
Mean666.50462
Median Absolute Deviation (MAD)26
Skewness0.01047643
Sum21504105
Variance1597.4453
MonotonicityNot monotonic
2024-09-21T08:33:43.737816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
667 1011
 
3.1%
665 337
 
1.0%
672 334
 
1.0%
670 331
 
1.0%
668 330
 
1.0%
674 327
 
1.0%
659 324
 
1.0%
678 320
 
1.0%
652 317
 
1.0%
671 317
 
1.0%
Other values (275) 28316
87.8%
ValueCountFrequency (%)
509 1
 
< 0.1%
518 1
 
< 0.1%
520 1
 
< 0.1%
524 1
 
< 0.1%
525 1
 
< 0.1%
526 1
 
< 0.1%
530 2
< 0.1%
532 1
 
< 0.1%
533 1
 
< 0.1%
534 3
< 0.1%
ValueCountFrequency (%)
820 1
 
< 0.1%
817 1
 
< 0.1%
813 1
 
< 0.1%
811 1
 
< 0.1%
810 1
 
< 0.1%
807 3
< 0.1%
805 2
< 0.1%
804 1
 
< 0.1%
803 1
 
< 0.1%
802 3
< 0.1%

Moved
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
31308 
1
 
956

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

Length

2024-09-21T08:33:43.776411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:43.805393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31308
97.0%
1 956
 
3.0%

InArea
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
30983 
0
 
1281

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Length

2024-09-21T08:33:43.835559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:43.863882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Most occurring characters

ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30983
96.0%
0 1281
 
4.0%

Ins
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
21089 
1
11175 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Length

2024-09-21T08:33:43.894681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:43.923480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Most occurring characters

ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21089
65.4%
1 11175
34.6%

Branch
Categorical

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
B4
5633 
B2
5345 
B3
2844 
B1
2819 
B5
2752 
Other values (14)
12871 

Length

Max length3
Median length2
Mean length2.2517047
Min length2

Characters and Unicode

Total characters72649
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB17
2nd rowB2
3rd rowB3
4th rowB1
5th rowB1

Common Values

ValueCountFrequency (%)
B4 5633
17.5%
B2 5345
16.6%
B3 2844
8.8%
B1 2819
8.7%
B5 2752
8.5%
B15 2235
 
6.9%
B16 1534
 
4.8%
B6 1438
 
4.5%
B7 1413
 
4.4%
B8 1341
 
4.2%
Other values (9) 4910
15.2%

Length

2024-09-21T08:33:43.956174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b4 5633
17.5%
b2 5345
16.6%
b3 2844
8.8%
b1 2819
8.7%
b5 2752
8.5%
b15 2235
 
6.9%
b16 1534
 
4.8%
b6 1438
 
4.5%
b7 1413
 
4.4%
b8 1341
 
4.2%
Other values (9) 4910
15.2%

Most occurring characters

ValueCountFrequency (%)
B 32264
44.4%
1 11187
 
15.4%
4 6705
 
9.2%
2 5894
 
8.1%
5 4987
 
6.9%
3 3379
 
4.7%
6 2972
 
4.1%
7 2263
 
3.1%
8 1882
 
2.6%
9 843
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40385
55.6%
Uppercase Letter 32264
44.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11187
27.7%
4 6705
16.6%
2 5894
14.6%
5 4987
12.3%
3 3379
 
8.4%
6 2972
 
7.4%
7 2263
 
5.6%
8 1882
 
4.7%
9 843
 
2.1%
0 273
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
B 32264
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40385
55.6%
Latin 32264
44.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11187
27.7%
4 6705
16.6%
2 5894
14.6%
5 4987
12.3%
3 3379
 
8.4%
6 2972
 
7.4%
7 2263
 
5.6%
8 1882
 
4.7%
9 843
 
2.1%
0 273
 
0.7%
Latin
ValueCountFrequency (%)
B 32264
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 32264
44.4%
1 11187
 
15.4%
4 6705
 
9.2%
2 5894
 
8.1%
5 4987
 
6.9%
3 3379
 
4.7%
6 2972
 
4.1%
7 2263
 
3.1%
8 1882
 
2.6%
9 843
 
1.2%

Res
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
U
12681 
S
11506 
R
8077 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
U 12681
39.3%
S 11506
35.7%
R 8077
25.0%

Length

2024-09-21T08:33:43.989989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:44.020470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
u 12681
39.3%
s 11506
35.7%
r 8077
25.0%

Most occurring characters

ValueCountFrequency (%)
U 12681
39.3%
S 11506
35.7%
R 8077
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 32264
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 12681
39.3%
S 11506
35.7%
R 8077
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32264
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 12681
39.3%
S 11506
35.7%
R 8077
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 12681
39.3%
S 11506
35.7%
R 8077
25.0%

Dep
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1346082
Minimum0
Maximum28
Zeros7023
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:44.049822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7217969
Coefficient of variation (CV)0.80661028
Kurtosis4.5281284
Mean2.1346082
Median Absolute Deviation (MAD)1
Skewness1.096764
Sum68871
Variance2.9645847
MonotonicityNot monotonic
2024-09-21T08:33:44.082640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 9345
29.0%
0 7023
21.8%
3 5846
18.1%
1 4227
13.1%
4 2799
 
8.7%
5 1916
 
5.9%
6 630
 
2.0%
7 242
 
0.8%
8 130
 
0.4%
9 51
 
0.2%
Other values (11) 55
 
0.2%
ValueCountFrequency (%)
0 7023
21.8%
1 4227
13.1%
2 9345
29.0%
3 5846
18.1%
4 2799
 
8.7%
5 1916
 
5.9%
6 630
 
2.0%
7 242
 
0.8%
8 130
 
0.4%
9 51
 
0.2%
ValueCountFrequency (%)
28 1
 
< 0.1%
21 1
 
< 0.1%
20 2
 
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 3
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
11 14
< 0.1%

DepAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct24535
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2232.7602
Minimum0
Maximum484893.67
Zeros7024
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size252.2 KiB
2024-09-21T08:33:44.121197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1149.65
median1103.62
Q32439.53
95-th percentile7069.483
Maximum484893.67
Range484893.67
Interquartile range (IQR)2289.88

Descriptive statistics

Standard deviation6704.2923
Coefficient of variation (CV)3.0026925
Kurtosis1618.1745
Mean2232.7602
Median Absolute Deviation (MAD)1101.125
Skewness30.510648
Sum72037775
Variance44947535
MonotonicityNot monotonic
2024-09-21T08:33:44.166643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7024
 
21.8%
0.01 4
 
< 0.1%
15.24 3
 
< 0.1%
494.17 3
 
< 0.1%
920.99 3
 
< 0.1%
1218.19 3
 
< 0.1%
1117.85 3
 
< 0.1%
844.18 3
 
< 0.1%
902.96 3
 
< 0.1%
906.92 3
 
< 0.1%
Other values (24525) 25212
78.1%
ValueCountFrequency (%)
0 7024
21.8%
0.01 4
 
< 0.1%
0.06 1
 
< 0.1%
0.19 1
 
< 0.1%
0.38 1
 
< 0.1%
0.41 1
 
< 0.1%
0.45 1
 
< 0.1%
0.49 1
 
< 0.1%
0.53 1
 
< 0.1%
0.85 1
 
< 0.1%
ValueCountFrequency (%)
484893.67 1
< 0.1%
387684.61 1
< 0.1%
359618.47 1
< 0.1%
271421.4 1
< 0.1%
229528.59 1
< 0.1%
162596.53 1
< 0.1%
160811.78 1
< 0.1%
150025.47 1
< 0.1%
135312.56 1
< 0.1%
129348.34 1
< 0.1%

Inv
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
31429 
1
 
835

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32264
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

Length

2024-09-21T08:33:44.205853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-21T08:33:44.235567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 32264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31429
97.4%
1 835
 
2.6%

InvBal
Real number (ℝ)

SKEWED  ZEROS 

Distinct479
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1394.3178
Minimum-2214.92
Maximum8323796
Zeros31786
Zeros (%)98.5%
Negative2
Negative (%)< 0.1%
Memory size252.2 KiB
2024-09-21T08:33:44.269315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2214.92
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8323796
Range8326010.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation53170.759
Coefficient of variation (CV)38.133889
Kurtosis18816.158
Mean1394.3178
Median Absolute Deviation (MAD)0
Skewness124.87559
Sum44986269
Variance2.8271296 × 109
MonotonicityNot monotonic
2024-09-21T08:33:44.311617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31786
98.5%
8759.26 1
 
< 0.1%
9748.37 1
 
< 0.1%
2124.42 1
 
< 0.1%
395022.21 1
 
< 0.1%
5646.11 1
 
< 0.1%
36503.14 1
 
< 0.1%
16995.9 1
 
< 0.1%
14465.74 1
 
< 0.1%
229914.92 1
 
< 0.1%
Other values (469) 469
 
1.5%
ValueCountFrequency (%)
-2214.92 1
 
< 0.1%
-659.77 1
 
< 0.1%
0 31786
98.5%
0.92 1
 
< 0.1%
9.12 1
 
< 0.1%
34.45 1
 
< 0.1%
121.64 1
 
< 0.1%
172.58 1
 
< 0.1%
242.19 1
 
< 0.1%
286.18 1
 
< 0.1%
ValueCountFrequency (%)
8323796.02 1
< 0.1%
2547675.52 1
< 0.1%
1345442.96 1
< 0.1%
1196452.9 1
< 0.1%
1175774.34 1
< 0.1%
1057244.73 1
< 0.1%
1009028.65 1
< 0.1%
1002678.08 1
< 0.1%
797302.88 1
< 0.1%
779464.96 1
< 0.1%

Interactions

2024-09-21T08:33:39.434130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.515026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.392821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.238907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.148003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.019480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.944656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.781010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.744187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.565809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.398839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.331619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.185127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.126768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.971007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.862884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.803576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.632274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.551839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.394860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.301402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.122364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.012047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.939895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.809303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.754894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.586511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.465426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.549445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.424482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.269496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.180936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.051357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.976113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.815194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.774329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.597536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.431407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.363046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.218268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.158244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.003931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.975656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.834258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.664800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.584014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.425796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.331767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.155255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.044410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.971089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.842133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.786171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.619078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.495312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.579927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.455654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.300430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.211850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.082910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.006833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.846876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.805057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.627740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.462541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.394336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.248527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.189195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.034880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.007199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.865147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.694997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.614416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.457210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.360730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.187769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.075413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.003321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.873693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.815510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.649528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.525173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.610527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.485247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.330093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.242747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.113025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.037106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.880264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.834749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.656981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.493805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.425696image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.280307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.219224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.066292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.038146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.894932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.725367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.644572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.487371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.390615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.219328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.106831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.035014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.906086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.845460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.679959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.557429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.643269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.518591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.363601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.276700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.145772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.069610image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.914724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.868021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.689987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.526663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.459892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.314298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.252495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.099650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.071136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.926498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.759066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.677326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.519152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.421841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.254838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.140567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.069597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.939502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.877878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.712773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.588779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:16.676052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.549452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.466263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.308916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.177358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.101064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.947152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-21T08:33:18.928894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.792060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.718685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.562010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.435907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.350087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.180656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.106873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.962259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.900900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.748419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.614820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.578331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.414781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.330664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.173743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.007511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.905152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.779847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.718367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.581701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.533022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.367087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.213127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.145981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.201972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.052185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.962219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.826228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.753674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.596307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.550515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.383799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.214153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.140459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.996750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.936126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.782802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.653142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.612541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.448756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.364791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.207722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.041504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.938314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.815143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.752229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.616519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.567482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.401229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.246939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.176422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.234179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.083683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.994134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.860033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.785147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.627706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.583028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.415215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.244905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.173494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.027840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.968585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.813924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.694488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.645973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.479858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.396182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.240332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.072907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.970033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.848031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.784081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.650798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.598933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.432864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.278186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.208257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.267200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.116574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.026235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.893345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.818430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.660734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.616967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.447385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.277456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.206451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.061860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.002351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.847168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.731720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.678475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.512894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.428201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.272174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.104313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.002475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.882842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.816617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.683161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.632472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.464217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.311358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.238975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.298750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.147856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.058383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.925812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.850185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.692202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.649672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.478495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.308832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.238978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.093461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.034536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.878770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.766913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.710301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.543830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.461005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.304113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.136569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.033867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.916224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.848518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.715733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.663348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.496072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.343024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.267307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.330665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.177912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.087959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.957096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.883302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.720740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.681503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.507405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.338957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.269990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.124598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.066156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.909922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.799692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.741690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.572905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.491354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.334374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.165671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.063371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.948395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.879104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.746756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.694285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.525152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.374079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:40.297998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:17.362128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:18.208676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.118649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:19.988480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:20.913863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:21.751743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:22.712758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:23.537454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:24.368759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:25.301746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:26.154854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.096337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:27.940465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:28.832319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:29.773352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:30.603280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:31.522556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:32.364974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:33.273253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.092744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:34.980406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:35.909841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:36.779697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:37.724888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:38.556942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-21T08:33:39.404151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-21T08:33:44.361144image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ATMATMAmtAcctAgeAgeBranchCCCCBalCCPurcCDCDBalCRScoreCashBkChecksDDADDABalDepDepAmtDirDepHMOwnHMValILSILSBalIRAIRABalInAreaIncomeInsInvInvBalLOCLOCBalLOResMMMMBalMMCredMTGMTGBalMovedNSFNSFAmtPOSPOSAmtPhoneResSDBSavSavBalTeller
ATM1.0000.0300.1340.0000.0270.0820.0160.0200.1110.0400.0000.0710.2210.3920.0260.4000.0070.1310.0050.0120.0000.0170.0610.0260.2540.0000.1220.0750.0170.0270.0170.0000.1980.1960.1190.0110.0220.0000.1670.0480.1900.1470.0950.0170.0300.1600.0300.028
ATMAmt0.0301.000-0.0830.0110.0000.0160.0020.0320.015-0.0940.0020.0000.3670.0000.2380.5270.4790.0000.0000.0190.0000.0150.011-0.0380.0000.0000.0360.000-0.0400.0110.006-0.0040.028-0.140-0.0850.0120.0120.0000.0000.1580.3780.3790.2220.0000.0000.0240.122-0.002
AcctAge0.134-0.0831.0000.0190.0110.1060.1280.0520.0440.0090.0130.0220.1450.0740.052-0.0130.0610.1310.0130.0190.0260.0440.0530.0780.0410.0040.0310.0530.0350.0630.0740.0120.0900.1010.0580.0320.0430.0000.058-0.071-0.087-0.080-0.0280.0000.0260.0250.0140.012
Age0.0000.0110.0191.0000.0870.0160.0080.0010.000-0.0010.4230.0000.0070.0100.0170.0050.0160.0050.302-0.1600.0000.0010.0000.0070.000-0.1440.0040.0140.0050.0000.0050.2480.009-0.004-0.0080.0070.0070.0000.0000.0060.0120.013-0.0040.0100.0000.0080.0240.004
Branch0.0270.0000.0110.0871.0000.3280.0000.0610.0130.0120.0330.0000.0240.0150.0110.0090.0210.0080.1590.0220.0540.0170.0200.0000.0090.0650.1010.0610.0100.1020.0190.0820.0320.0180.0170.1080.0000.0000.0250.0000.0280.0220.0200.3050.0150.1730.0170.004
CC0.0820.0160.1060.0160.3281.0000.0180.4100.0670.0170.0050.0480.0980.1100.0070.0000.0220.0100.0330.0070.1770.1770.0890.0220.0200.0150.1470.0880.0060.1780.0550.0060.1150.1160.0670.1690.0100.0000.0940.0190.0050.0340.0350.0300.0490.0680.0110.019
CCBal0.0160.0020.1280.0080.0000.0181.0000.3800.0000.0140.0050.0000.1150.0240.0440.0360.0980.0000.0100.0080.0230.2900.0000.0660.0000.0010.0080.0200.0550.0350.288-0.0140.0230.0710.0460.0770.3140.0000.000-0.0470.0120.0180.0280.0030.0000.0090.0630.038
CCPurc0.0200.0320.0520.0010.0610.4100.3801.0000.0350.0330.0040.0000.0400.0460.0310.0020.0440.0000.0390.0100.1480.1450.0870.0810.0120.0010.0830.0670.0470.1110.107-0.0080.1790.1700.1020.2330.2090.0000.032-0.0330.0230.0290.0030.0150.0500.0590.067-0.004
CD0.1110.0150.0440.0000.0130.0670.0000.0351.0000.1200.0070.0190.0680.1550.0000.0770.0060.0500.0000.0090.0120.0630.0660.0270.0580.0000.2020.0480.0000.0000.0000.0000.1090.1060.0650.0050.0000.0000.0510.0180.0370.0240.0330.0000.1010.0640.0100.021
CDBal0.040-0.0940.009-0.0010.0120.0170.0140.0330.1201.0000.0000.000-0.1000.048-0.062-0.130-0.0980.0150.002-0.0020.000-0.0120.0360.0710.027-0.0010.0400.0390.0340.017-0.0070.0100.0660.1140.0700.000-0.0080.0290.000-0.054-0.083-0.081-0.0750.0110.0430.0000.094-0.048
CRScore0.0000.0020.0130.4230.0330.0050.0050.0040.0070.0001.0000.0000.0050.0000.011-0.0010.0070.0000.084-0.0740.0000.0020.0000.0050.009-0.0760.0040.006-0.0010.0000.0020.1070.000-0.002-0.0030.0000.0050.0220.0100.007-0.006-0.006-0.0040.0040.0170.0130.0080.001
CashBk0.0710.0000.0220.0000.0000.0480.0000.0000.0190.0000.0001.0000.0000.0580.0000.1000.0000.0470.0090.0000.0000.0000.0210.0000.0230.0120.0410.0120.0000.0220.0000.0000.0370.0120.0000.0170.0000.0000.0790.0120.0210.0220.0200.0020.0130.0160.0000.123
Checks0.2210.3670.1450.0070.0240.0980.1150.0400.068-0.1000.0050.0001.0000.3530.5600.7370.7950.3220.0180.0790.0820.0630.018-0.0220.1500.0070.0710.030-0.0330.1280.0760.0030.046-0.092-0.0550.0750.0260.0000.0960.1330.2320.2360.2970.0080.0090.0880.0870.452
DDA0.3920.0000.0740.0100.0150.1100.0240.0460.1550.0480.0000.0580.3531.0000.0440.3580.0170.3080.0110.0470.0130.0260.1140.0360.3400.0000.1850.1160.0270.0340.0160.0000.2700.2760.1520.0400.0200.0000.1470.0410.0850.0540.0730.0120.0590.0780.0360.237
DDABal0.0260.2380.0520.0170.0110.0070.0440.0310.000-0.0620.0110.0000.5600.0441.0000.4730.7230.0350.0000.2120.0000.0380.007-0.0210.0040.0130.0700.023-0.0260.0150.0450.0050.007-0.085-0.0570.0000.0360.0310.011-0.0300.0700.0790.0480.0080.0000.0110.1000.365
Dep0.4000.527-0.0130.0050.0090.0000.0360.0020.077-0.130-0.0010.1000.7370.3580.4731.0000.7800.2450.0030.0470.0440.0430.038-0.0710.1520.0000.1080.033-0.0520.0420.025-0.0040.109-0.173-0.1040.0120.0020.0000.2030.2320.3480.3440.3630.0110.0220.1060.0540.438
DepAmt0.0070.4790.0610.0160.0210.0220.0980.0440.006-0.0980.0070.0000.7950.0170.7230.7801.0000.0000.0030.1310.0210.0640.018-0.0330.0000.0090.0270.046-0.0310.0460.0830.0030.033-0.107-0.0640.0350.0550.0000.0150.1440.2460.2540.2620.0000.0000.0000.0860.464
DirDep0.1310.0000.1310.0050.0080.0100.0000.0000.0500.0150.0000.0470.3220.3080.0350.2450.0001.0000.0000.0310.0130.0120.0190.0000.1310.0000.0700.0180.0030.0000.0090.0000.0550.0620.0280.0060.0080.0040.0140.0110.0830.0830.0680.0000.0150.0000.0050.042
HMOwn0.0050.0000.0130.3020.1590.0330.0100.0390.0000.0020.0840.0090.0180.0110.0000.0030.0030.0001.0000.0570.0100.0090.0120.0000.0000.1930.0000.0150.0000.0440.0140.7400.0000.0000.0020.1780.0120.0000.0080.0070.0000.0000.0000.0050.0000.0000.0030.000
HMVal0.0120.0190.019-0.1600.0220.0070.0080.0100.009-0.002-0.0740.0000.0790.0470.2120.0470.1310.0310.0571.0000.000-0.0000.0000.0070.0150.7530.0640.0060.0130.0220.013-0.2400.0000.000-0.0060.0000.0180.0230.000-0.0210.0000.002-0.0140.0200.0000.0000.0190.057
ILS0.0000.0000.0260.0000.0540.1770.0230.1480.0120.0000.0000.0000.0820.0130.0000.0440.0210.0130.0100.0001.0001.0000.0160.0000.0260.0150.0140.0110.0000.1730.0240.0000.0000.0000.0000.0660.0260.0000.0000.0050.0000.0000.0080.0110.0010.0260.0000.044
ILSBal0.0170.0150.0440.0010.0170.1770.2900.1450.063-0.0120.0020.0000.0630.0260.0380.0430.0640.0120.009-0.0001.0001.0000.0340.0130.023-0.0020.0310.0320.0130.1740.159-0.0020.0340.0060.0080.0670.0650.0000.0000.005-0.017-0.015-0.0050.0090.0170.0250.0260.035
IRA0.0610.0110.0530.0000.0200.0890.0000.0870.0660.0360.0000.0210.0180.1140.0070.0380.0180.0190.0120.0000.0160.0341.0000.1750.0250.0000.1120.2140.0270.0720.0380.0060.1170.1180.0770.0410.0170.0050.0370.0000.0240.0210.0110.0040.0900.0370.0350.019
IRABal0.026-0.0380.0780.0070.0000.0220.0660.0810.0270.0710.0050.000-0.0220.036-0.021-0.071-0.0330.0000.0000.0070.0000.0130.1751.0000.0280.0010.0290.0360.1650.0370.0590.0040.0360.1120.0740.0070.0310.0000.000-0.037-0.050-0.048-0.0380.0110.0280.0000.065-0.024
InArea0.2540.0000.0410.0000.0090.0200.0000.0120.0580.0270.0090.0230.1500.3400.0040.1520.0000.1310.0000.0150.0260.0230.0250.0281.0000.0000.0600.0330.0180.0520.0000.0070.0970.0990.0520.0000.0000.0000.0620.0100.0470.0370.0280.0000.0140.0850.0060.100
Income0.0000.0000.004-0.1440.0650.0150.0010.0010.000-0.001-0.0760.0120.0070.0000.0130.0000.0090.0000.1930.7530.015-0.0020.0000.0010.0001.0000.0090.0160.0070.0160.001-0.0750.000-0.010-0.0100.0000.0070.0090.000-0.0040.0030.0020.0010.0090.0120.0000.012-0.005
Ins0.1220.0360.0310.0040.1010.1470.0080.0830.2020.0400.0040.0410.0710.1850.0700.1080.0270.0700.0000.0640.0140.0310.1120.0290.0600.0091.0000.0980.0180.0000.0070.0000.1620.1580.0950.0000.0080.0000.0680.0200.0390.0250.0600.0090.0720.1510.0820.022
Inv0.0750.0000.0530.0140.0610.0880.0200.0670.0480.0390.0060.0120.0300.1160.0230.0330.0460.0180.0150.0060.0110.0320.2140.0360.0330.0160.0981.0000.0960.0540.0400.0040.1210.1190.0770.0380.0170.0000.0250.0000.0120.0180.0000.0290.0360.0000.0040.024
InvBal0.017-0.0400.0350.0050.0100.0060.0550.0470.0000.034-0.0010.000-0.0330.027-0.026-0.052-0.0310.0030.0000.0130.0000.0130.0270.1650.0180.0070.0180.0961.0000.0190.0440.0130.0270.1000.0550.0240.0300.0000.000-0.017-0.034-0.033-0.0130.0060.0000.0050.010-0.027
LOC0.0270.0110.0630.0000.1020.1780.0350.1110.0000.0170.0000.0220.1280.0340.0150.0420.0460.0000.0440.0220.1730.1740.0720.0370.0520.0160.0000.0540.0191.0000.2890.0000.0760.0750.0540.1830.0380.0000.0130.0000.0000.0150.0130.0150.0000.0040.0180.059
LOCBal0.0170.0060.0740.0050.0190.0550.2880.1070.000-0.0070.0020.0000.0760.0160.0450.0250.0830.0090.0140.0130.0240.1590.0380.0590.0000.0010.0070.0400.0440.2891.0000.0070.0400.0630.0440.0850.1810.0000.000-0.010-0.037-0.034-0.0140.0100.0030.019-0.0020.039
LORes0.000-0.0040.0120.2480.0820.006-0.014-0.0080.0000.0100.1070.0000.0030.0000.005-0.0040.0030.0000.740-0.2400.000-0.0020.0060.0040.007-0.0750.0000.0040.0130.0000.0071.0000.009-0.008-0.0050.0090.0050.0000.0060.002-0.002-0.001-0.0070.0150.0000.000-0.001-0.002
MM0.1980.0280.0900.0090.0320.1150.0230.1790.1090.0660.0000.0370.0460.2700.0070.1090.0330.0550.0000.0000.0000.0340.1170.0360.0970.0000.1620.1210.0270.0760.0400.0091.0000.9890.5950.0500.0200.0000.0650.0240.0560.0300.0460.0370.1140.1180.0000.044
MMBal0.196-0.1400.101-0.0040.0180.1160.0710.1700.1060.114-0.0020.012-0.0920.276-0.085-0.173-0.1070.0620.0000.0000.0000.0060.1180.1120.099-0.0100.1580.1190.1000.0750.063-0.0080.9891.0000.5950.0490.0500.0320.063-0.064-0.109-0.106-0.1000.0280.1110.126-0.108-0.080
MMCred0.119-0.0850.058-0.0080.0170.0670.0460.1020.0650.070-0.0030.000-0.0550.152-0.057-0.104-0.0640.0280.002-0.0060.0000.0080.0770.0740.052-0.0100.0950.0770.0550.0540.044-0.0050.5950.5951.0000.0320.0320.0210.036-0.038-0.067-0.065-0.0580.0170.0700.068-0.060-0.054
MTG0.0110.0120.0320.0070.1080.1690.0770.2330.0050.0000.0000.0170.0750.0400.0000.0120.0350.0060.1780.0000.0660.0670.0410.0070.0000.0000.0000.0380.0240.1830.0850.0090.0500.0490.0321.0000.0840.0390.0160.0000.0000.0020.0160.0260.0040.0000.0000.012
MTGBal0.0220.0120.0430.0070.0000.0100.3140.2090.000-0.0080.0050.0000.0260.0200.0360.0020.0550.0080.0120.0180.0260.0650.0170.0310.0000.0070.0080.0170.0300.0380.1810.0050.0200.0500.0320.0841.0000.0000.000-0.016-0.034-0.032-0.0230.0000.0000.0160.0120.010
Moved0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0220.0000.0000.0000.0310.0000.0000.0040.0000.0230.0000.0000.0050.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0320.0210.0390.0001.0000.0000.0000.0000.0290.0000.0000.0040.0000.0310.000
NSF0.1670.0000.0580.0000.0250.0940.0000.0320.0510.0000.0100.0790.0960.1470.0110.2030.0150.0140.0080.0000.0000.0000.0370.0000.0620.0000.0680.0250.0000.0130.0000.0060.0650.0630.0360.0160.0000.0001.0000.2980.0930.0500.1560.0000.0250.0060.0060.104
NSFAmt0.0480.158-0.0710.0060.0000.019-0.047-0.0330.018-0.0540.0070.0120.1330.041-0.0300.2320.1440.0110.007-0.0210.0050.0050.000-0.0370.010-0.0040.0200.000-0.0170.000-0.0100.0020.024-0.064-0.0380.000-0.0160.0000.2981.0000.1750.1700.2240.0060.0130.000-0.0700.128
POS0.1900.378-0.0870.0120.0280.0050.0120.0230.037-0.083-0.0060.0210.2320.0850.0700.3480.2460.0830.0000.0000.000-0.0170.024-0.0500.0470.0030.0390.012-0.0340.000-0.037-0.0020.056-0.109-0.0670.000-0.0340.0000.0930.1751.0000.9960.3390.0000.0250.0290.0000.046
POSAmt0.1470.379-0.0800.0130.0220.0340.0180.0290.024-0.081-0.0060.0220.2360.0540.0790.3440.2540.0830.0000.0020.000-0.0150.021-0.0480.0370.0020.0250.018-0.0330.015-0.034-0.0010.030-0.106-0.0650.002-0.0320.0290.0500.1700.9961.0000.3320.0000.0100.0360.0060.048
Phone0.0950.222-0.028-0.0040.0200.0350.0280.0030.033-0.075-0.0040.0200.2970.0730.0480.3630.2620.0680.000-0.0140.008-0.0050.011-0.0380.0280.0010.0600.000-0.0130.013-0.014-0.0070.046-0.100-0.0580.016-0.0230.0000.1560.2240.3390.3321.0000.0000.0000.000-0.0520.163
Res0.0170.0000.0000.0100.3050.0300.0030.0150.0000.0110.0040.0020.0080.0120.0080.0110.0000.0000.0050.0200.0110.0090.0040.0110.0000.0090.0090.0290.0060.0150.0100.0150.0370.0280.0170.0260.0000.0000.0000.0060.0000.0000.0001.0000.0050.0170.0000.018
SDB0.0300.0000.0260.0000.0150.0490.0000.0500.1010.0430.0170.0130.0090.0590.0000.0220.0000.0150.0000.0000.0010.0170.0900.0280.0140.0120.0720.0360.0000.0000.0030.0000.1140.1110.0700.0040.0000.0040.0250.0130.0250.0100.0000.0051.0000.0830.0220.008
Sav0.1600.0240.0250.0080.1730.0680.0090.0590.0640.0000.0130.0160.0880.0780.0110.1060.0000.0000.0000.0000.0260.0250.0370.0000.0850.0000.1510.0000.0050.0040.0190.0000.1180.1260.0680.0000.0160.0000.0060.0000.0290.0360.0000.0170.0831.0000.0820.044
SavBal0.0300.1220.0140.0240.0170.0110.0630.0670.0100.0940.0080.0000.0870.0360.1000.0540.0860.0050.0030.0190.0000.0260.0350.0650.0060.0120.0820.0040.0100.018-0.002-0.0010.000-0.108-0.0600.0000.0120.0310.006-0.0700.0000.006-0.0520.0000.0220.0821.0000.042
Teller0.028-0.0020.0120.0040.0040.0190.038-0.0040.021-0.0480.0010.1230.4520.2370.3650.4380.4640.0420.0000.0570.0440.0350.019-0.0240.100-0.0050.0220.024-0.0270.0590.039-0.0020.044-0.080-0.0540.0120.0100.0000.1040.1280.0460.0480.1630.0180.0080.0440.0421.000

Missing values

2024-09-21T08:33:40.364601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-21T08:33:40.571814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AcctAgeDDADDABalCashBkChecksDirDepNSFNSFAmtPhoneTellerSavSavBalATMATMAmtPOSPOSAmtCDCDBalIRAIRABalLOCLOCBalILSILSBalMMMMBalMMCredMTGMTGBalCCCCBalCCPurcSDBIncomeHMOwnLOResHMValAgeCRScoreMovedInAreaInsBranchResDepDepAmtInvInvBal
00.31419.2700000.0000110233.721106.7400.000000.000.000.000.0000.001483.650016111.08963696011B17R21170.0600.0
10.711986.8101100.000000.001268.8800.000000.000.000.000.0000.0010.0010417.08751674010B2R1446.9300.0
24.100.0000000.000000.0000.0000.000000.000.000.000.0000.0010.00003018.59760640011B3S00.0000.0
30.511594.8401000.00011425.061278.0700.000000.000.000.000.0000.00165.760012517.514544672010B1S11144.2400.0
46.712813.4502000.000512716.5500.0000.000000.000.000.000.0000.0000.00002516.010146648011B1S21208.9400.0
512.311069.78013100.002900.0000.0000.000000.000.000.000.0000.00138.62001903.010755662011B7U56813.5800.0
68.811437.57012100.000000.001391.6300.000000.000.000.000.00194539.95185202.99005513.512857659010B1U22237.6900.0
79.311683.2802100.00001967.041276.1200.000000.000.000.000.0000.0010.00001304.59958675011B5U3795.8500.0
80.81190.0301015.6500111.4711582.3400.000000.000.000.000.0000.0000.00012004.010740642010B14S3880.2500.0
90.91462.1204000.000219010.401863.11123.130000.000.000.000.0000.0000.00015404.012973667011B6S21049.5700.0
AcctAgeDDADDABalCashBkChecksDirDepNSFNSFAmtPhoneTellerSavSavBalATMATMAmtPOSPOSAmtCDCDBalIRAIRABalLOCLOCBalILSILSBalMMMMBalMMCredMTGMTGBalCCCCBalCCPurcSDBIncomeHMOwnLOResHMValAgeCRScoreMovedInAreaInsBranchResDepDepAmtInvInvBal
322543.7116.38031114.917315.101388.76297.20000.000.0000.0000.00000.000.00003117.510225640010B2S4952.0200.0
322558.6146593.6309100.001100.0000.0000.00000.01112306.8500.00147406.48000.01108200.42004518.520770721010B3U355203.5800.0
3225613.014062.53016000.00815602.7600.0000.00000.000.0000.00117094.72000.000.00004118.010769698010B1S37383.5800.0
322573.900.0000000.00000.0000.0000.00000.010.0019912.7800.00000.01178531.73006017.011461716010B2S00.0000.0
322582.211037.9802100.000144.67172.6400.00000.000.0000.0000.00000.000.00001205.59555760010B4R1679.4300.0
322590.611073.8808100.00000.001307.4900.00000.000.0000.0000.00000.000.00005205.012937634010B16R2978.5400.0
322603.900.0000000.00000.0000.0000.00000.000.0000.0000.00000.010.00006916.512437586011B1S00.0000.0
3226119.112139.5907100.02000.0011346.4100.00000.000.0000.0000.00000.000.00003516.510748596011B2S33226.8700.0
322622.100.0000000.00014252.1300.0000.00000.000.0000.0000.00000.000.00004207.511832680011B5S00.0000.0
3226313.000.0000000.00000.0000.0000.011600000.000.0000.00114812.47000.011536.43012203.510939600000B4U00.0000.0